The goal

I’d like to be able to write my courses and tutorials in one document, then convert them to slides / pdfs / ebooks / web pages at the click of a button.
On a second button click, I’d like these docs to be uploaded on the web to make them open access, versionable and fit for multi-authoring.

In the previous blog post, I presented asciidoc as a promising technology to create easily open courses and tutorials.
It allows to write clean, minimum text files which then convert to web pages, pdf, ebooks and slides at a click of a button.

Just like you can write Word documents in MS Word or in Open Office, many tools exist to work on asciidoc.
I am still trying to figure out which one would best fit my needs. Because I program in Java, I looked at Java-based solutions because I can more easily adapt them to my needs.

AsciiDocFX?

I tried first AsciiDocFX, based on the emerging JavaFX technology. You can install it easily for Mac, Windows and Linux. Try it!
The great part is that you have an instant preview in html or slides, as you type.

A workaround is to first download the content of the web link as a file, then embed this file into the asciidoc.
I could not figure how to manage this from AsciiDocFX. It made me fear that AsciidocFX was too hard to customize, so I searched for a more flexible solution.

AsciiDocJ?

This is another Java version of the tools used to convert an asciidoc into web pages, pdf and the rest.
AsciiDocJ is not a software you install, this is a programming tool to be used in a programming environment.

I can use it from NetBeans, which is my favorite programming editor

Much less user friendly than AsciiDocFX (no preview of your docs), but I could live with that.

Provides full flexibility to manipulate the documents, by using code.

Web based pics can’t be embedded in my doc?
No problem, I can write some additional code which scans my doc, finds these web links and apply the necessary steps to make them right.
I am more confident that other bumps on the road of processing the conversion of docs (footnotes in books? transitions in slides? custom styling?) can be dealt with.

Next steps

AsciiDocJ is configured through Maven rather than in pure java code. Maven is a protocol written in Java to customize the assembly of the files of a project: zipping them, sending them to a server, executing them…
The trick is, Maven is configured through a quite complex XML file. I need some time to get acquainted to that.
If you read this post in html, slides, or pdf form this means I’ve made some progresses!

But nobody in the team knows how to code an app. Nobody in the room, really, knows how to create an app. There is this hope that maybe, a geek friend from an engineering school will give a hand to code the app… or there is also the hope to convince a banker or a business angel to finance the prototype of an app… In practice, this almost never happens. Tons of excellent projects are abandoned or drag on forever because apps never materialize.

But what if apps could be created by everyone? Let’s dream a bit

1) everyone invited: not just those who already know how to code. Like, supra easy to create.

2) the app should be fantastic: not a web page packaged as a fake app, but a real app with access to all functionalities of the phone that it needs (camera, GPS, contacts…)

3) the app should be available on the Playstore (for Android), the App Store (for Apple), and Windows Phone’s Store.

4) and the tool to create the app should be free if possible. No yearly subscription to pay, no fee if the app is made for commercial use…

It can be done: Towards a MOOC “Creating mobile apps” for entrepreneurs

I am really excited to create a MOOC teaching anyone how to create mobile apps for Android, iOS and Windows Phone. It will open on Sept 2015. The objective of this MOOC is to take participants from a very basic starting point (no coding skills) to the creation of their own functional, native app available in Android, Apple and Windows Phone versions, and this in just 30 hours and for free. So that we see every entrepreneur able to launch their own apps.

The impetus for co-organizing this conference was the realization that Twitter is used in many different corners in academia, and yet there is little interdisciplinary communication on it.

Researchers are not always aware of how Twitter is used in slightly or totally different ways across the scientific spectrum. Great collaborations, or at least new insights for research, could be born from a day of exchange on the varieties of ways Twitter is used in academic research.

Next steps: offer an API, and continue researching on the detection of other semantic features of interest. Umigon already includes one: the detection of promoted discourse in tweets. Watch this space of follow me @seinecle for news!

This is going to be an evolving blog post retracing my current attempt at dealing with a dataset of 65 gigabytes. It will often look silly – that’s because I am not a programmer by training, and I make an effort at honestly recording the steps I took – including all mistakes and “doooohh!” moments.

See the bottom of the post for explanations on some questions. Add yours in the comments if you wish, I’ll do my best to respond.

I do this for the goal of exploring this dataset visually (an interesting methodological question I find) – and maybe foremost, to learn how to work with big datasets in practice. That’s harder than I thought.

The dataset:

It is delivered by post on a external hard drive containing a hierarchy of folders containing csv files (in various formatting) and excel files containing weekly data on product purchases in drugstores and shopping malls – collected across 10 years in participating stores in the US. The size of each file ranges from a couple of megabytes (Mb) to ~ 800 Mb. In total they make ~ 160 Gb, of which only 65 Gb I’ll end up using.

COUNTER OF COSTS SO FAR:

ACHIEVED SO FAR:

The files have been imported into a database.

Early november 2013

– delivery of the dataset (160Gb) on a 500Gb hard drive. – reading of the 75 pages pdf coming with the dataset. The datasets contains several different aspects, I realize I’ll start using a portion of it, making 65Gb. – copy of the dataset on the hard drive of my laptop (450Gb, spinning disk). Note: the laptop has a 2nd hard drive where the OS runs (SSD, 120Gb, almost full). – I write Java code to parse the files and import them into a Mongo database stored on my 450Gb hard drive, using the wonderfully helpful Morphia (makes the syntax so easy). – First attempts at importing: I realize that the database will be much bigger in size than the original flat files. Why? I investigate on StackOverflow and get to reduce the size of the future db significantly. – Still, I don’t know the final size of the db, so there is the risk that my hard drive will get full. I buy a 1 Terabyte / USB 3.0 external hard drive (Seagate, 70 euros at my local store).

Mid November 2013

– First attempts to import the Excel / csv files into MongoDB on this external hard drive. The laptop grinds to a halt after 2 hours of so: memory issues. What, on my 16Gb RAM laptop? The cause: by design, MongoDB will use all the memory available on the system if it needs it. It’s supposed to leave enough RAM for other processes but apparently it does not. I feel stuck. Oh wait, running MongoDB on a virtual machine would allow for allocating a specific amount of RAM to it? I tried Oracle’s Virtual Box but long story short, I can’t run a 64b virtual machine on my 64b laptop because a parameter in my BIOS should be switched on to allow for it, but my BIOS does not feature this parameter (and I won’t flash a BIOS, that’s beyond what I feel able to).

– At this point I realize that the external hard drive I bought won’t serve me here. I need a distant server for the database where Mongo willl sit alone. Or were there other options to keep the data locally?

End November 2013

– I try to rent a server from OVH (13 euros for a month + 13 euros setup costs: 1 Terabyte server with a small processor from Kimsufi, their low cost offer). I don’t get access to it in the following 3 days, and give up. Got a refund later.

– I rent a server (at ~ 40 euros per month, no setup cost) with 2 Terabyte hard drives, 24Gb of RM (!!) and a high performing processor (i9720) from Hetzner’s auction site. Sounds dodgy and too good to be true, yet I get access to it within 3 hours, install Debian and Mongo on it (easier than I thought, given that I am a Linux noob).

– Re-run my Java code on my laptop for importing the Excel/csv files onto this distant server. New bottleneck: it takes ages for the data to transfer from my wifi to the server. Of course…

– I rent a second server (at ~ 40 euros per month, still at Hetzner), in the same geographical region as the first, where I’ll put the data and run my Java code from. – Start uploading the data to it: takes ages (more than two weeks at this pace).

Early December 2013

– Went to my university to benefit from their transfer speed. After some hicups I got the 65Gb to transfer from my laptop to one of the remote servers I rented in just a couple of hours. – Starting the import of these 65Gb of csv / Excel files from this server to the Mongodb server. Monitoring the thing since the last 30 minutes, I see that already 60,000,000 917,000,000 (close to 1 billion!!) weekly purchase data transactions have transferred to the db – and counting! (one transaction looks like “this week, 45 packs of Guiness were bought at the store XXX located at Austin, Texas for a total of 200$”). Big data here I come! For some reason the stores descriptions didn’t get stored yet though. I’ll see that later. Very excited about the 1 billion transaction thing. Also worried on how to query this. We’ll see. – For some reason the database crashed after 1,1 billion transactions imported. Trying to relaunch the import where it stopped, I accidentally drop (delete) the database. Oooops. – Before relaunching the import, I optimize a bit the code, clear a bug, and go! – 14 hours that this new import has started. 2,949 stores found and stored, 138,985 products found and stored. And 1,3 billion transactions found and stored, and counting. Wow. No crash, looks good. – 2 days after it started, the import has finished without a crash! 2.29 billion “weekly purchase data” entries were found and stored in the db. The csv / Excel files take 65Gb of disk space, but once imported in the db the same data takes 400 Gigabytes of space. Wow. Next step: building indexes and start a first query.

QUESTIONS:

– Why not using university infrastructures?

I am transitioning between two universities (from Erasmus University Rotterdam to EMLyon Business School) at the moment, that’s not the right moment to ask for the set-up of a server, which could take weeks anyway. When arriving at EMLyon I’ll reconsider my options. The other reason is that I want to learn how “big data” works in practice. My big dataset is still smallish, and I already run into so many issues. So I am happy to go through it, as it will give me a better comprehension of what’s involved in dealing with the next scale: terabytes. I feel that this first hand knowledge will give me to teach the students in a better way, and that I will make more informed choices when dealing with experts (IT admins from the university or the CNRS) when comes the moment to launch larger scale projects in big data.

– Why MongoDB?

I was just seduced by the easyness of their query syntax. That’s horrifying as a decision parameter, I know. Still, I stand by it. I feel that it is indeed a determining factor because if the underlying performance is good enough (I’ll see that), then as a coder I can choose the db system which is the less painful / nicest to use (though I don’t use it myself, the MongoDB javascript console is I think a main driver behind the adoption of Mongo as a default for the Node.js community, I think). And with the Morphia library added to it, Mongo for Java is just a breeze to use: create POJOs, save POJOs, query POJOs. That’s it:

Of course, I’ll see with this current experiment if Mongo fits the job or not in terms of performance. If it doesn’t, I’ll explore Neo4J or SQL (in this order).

– Why not Amazon services?

Yes, yes. I am constrained by my attachement to MongoDB here. I would have run MongoDB on Amazon and all would have been fine, maybe. But the instructions on how to run Mongo on Amazon EC2 got me scared.

Yesterday I wrote a plugin that imports vector shapes of country maps (originally in .shp format) into Gephi. It is easy to think that not just 2D shapes like maps, but 3D, dynamic (time evolving) shapes could also be easily imported in Gephi. Because Gephi handles x, y and z coordinates, and handles time-dependent attributes too. So we’ve got all we need to view 3D worlds in Gephi. Here is how I would do it:

– write a parser of 3D shapes formats (DXF, X3D…).
– add the shapes to the graph. Each vector is two nodes and an edge connecting them. Putting that into Gephi is as simple as:

Possible extensions

Yes, the code above would just give you wireframes. Already a good start. I am out of my league here, but I think that new shaders can be written and added to Gephi’s JOGL engine to accomodate for textures, etc. No?

We also need to write some code for mouse movements, to allow for the exploration of the scene in 3D. Not trivial, but this has been implemented in many languages already, so that should be easy to port.

Also, there is no video export function to record animations made in Gephi at the moment, and that’s a pity because movies of 3D animations of vector shapes in Gephi would then become possible. But that’s something that will arrive at some point.

This plugin is useful when you have a network with geolocalized agents. A plugin released by Alexis Jacomy already makes it possible to display your networks according to geographical coordinates. Now you can add country borders as a background!

You can download this plugin directly from your Gephi software on your computer: go to Tools -> Plugins -> Available plugins. Click on “Check for updates” and then look for “Map of Countries” in the list.